Abstract
When choosing between options, we must solve an important binding problem. The values of the options must be associated with other information, including the action needed to select them. We hypothesized that the brain solves this binding problem through use of distinct population subspaces. We examined responses of single neurons in five value-sensitive regions in rhesus macaques performing a risky choice task. In all areas, neurons encoded the values of both possible options, but used semi-orthogonal coding subspaces associated with left and right options, which served to link options to their positions in space. We also observed a covariation between subspace orthogonalization and behavior: trials with less orthogonalized subspaces were associated with greater likelihood of choosing the less valued option. These semi-orthogonal subspaces arose from a combination of linear and non-linear mixed selective neurons. By decomposing the neural geometry, we show this combination of selectivity achieves a code that balances binding/separation and generalization. These results support the hypothesis that binding operations serve to convert high-dimensional codes to multiple low-dimensional neural subspaces to flexibly solve decision problems.
Fine, J. M., Johnston, W. J., Yoo, S. B. M., Ebitz, R. B., & Hayden, B. Y. (2022). Subspace orthogonalization as a mechanism for binding values to space. arXiv, 2205.06769.[LINK]
Speaker: Jiawei Zeng
Time: 9:30 am, 2022/06/10
Location: CIBR Phase I South, Floor 2
